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Are We Really Benchmarking Forecasting Models? The Impact of Preprocessing on Time Series Performance — arXiv:2610.09096

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.09096
License (as archived): CC0-1.0
Archived: 2026-10-08T11:56:21.966Z
SHA-256 of the archived text: 1ced99ca8ff3d3ff3b092b1c5825c9c2356aee45d113413719419af3352628d5
Stable URL of this page: https://metraipolis.com/documento/dc_fa0876fe0784

How to cite

Are We Really Benchmarking Forecasting Models? The Impact of Preprocessing on Time Series Performance — arXiv:2610.09096 — arXiv (en), https://arxiv.org/abs/2610.09096 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_fa0876fe0784 (Metraipolis archive dc_fa0876fe0784, sha256 1ced99ca8ff3d3ff3b092b1c5825c9c2356aee45d113413719419af3352628d5)

BibTeX

@misc{dcfa0876fe0784,
  title = {Are We Really Benchmarking Forecasting Models? The Impact of Preprocessing on Time Series Performance — arXiv:2610.09096},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.09096},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_fa0876fe0784; sha256 1ced99ca8ff3d3ff3b092b1c5825c9c2356aee45d113413719419af3352628d5},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.09096. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

While established literature underscores the pivotal role of preprocessing in forecasting accuracy, this stage remains largely overlooked in current research. Modern benchmarks typically resort to simple scaling, failing to account for critical transformations required to address nonstationarity, such as differencing. This omission creates a significant structural preprocessing bias that favors models with built-in data treatments while obscuring the true potential of simpler architectures. We study this effect through a preprocessing-aware benchmark that evaluates 11 forecasting models across 16 reversible preprocessing pipelines on 29,000 M4 time series. Our results identify preprocessing as a key driver of forecasting performance. Optimizing preprocessing per series yields gains of approximately 27\% to 87\% across all evaluated models, with architectures lacking internalized preprocessing experiencing the most substantial improvements. This allows simpler architectures to become highly competitive with complex, state-of-the-art models in modern forecasting benchmarks. All resources and experimental results from this benchmark are stored in a comprehensive metadataset to support future metalearning tasks.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.LG, nuovi lavori del giorno).
Categorie arXiv: Machine Learning (cs.LG).
Pagina del lavoro: https://arxiv.org/abs/2610.09096 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T10:50:37.655Z dalla pagina https://arxiv.org/list/cs.LG/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).